The development of modern digital video technology has brought significant enhancement in the video quality for consumers, such as in DVD players and in digital TVs (DTV) compared to the analog TV systems. However, such digital video systems only enhance the video quality in terms of signal to noise ratio (SNR) and resolution, without regard to other important issues relating to video enhancement. Such issues include contrast enhancement, brightness enhancement, and detail enhancement. Generally, video enhancement processes comprise a collection of techniques that seek to improve the visual appearance of video when displayed. This primarily includes gray level and contrast manipulation, noise reduction, edge crispening and sharpening. Compared to image restoration, video or image enhancement methods neither increase the inherent information content in the data nor require mathematical modeling. The basic principle of video enhancement is to manipulate a given sequence of images so that their appearance on display media can be improved. Because quantifying the criteria for enhancement is difficult, conventional video enhancement techniques are empirical and require interactive procedures to obtain satisfactory results.
Among the techniques for video enhancement, contrast enhancement is important because it plays a fundamental role in the overall appearance of an image to human being. A human being's perception is sensitive to contrast rather than the absolute values themselves. Hence, it is natural to enhance the contrast of an image in order to provide a good looking image to human beings.
Contrast enhancement involves considering the overall appearance of a given image rather than local appearances such as edge crispening or peaking. There are conventional models of contrast enhancement, and some examples include the root law and the logarithmic law. Image enhancement by contrast manipulation has been performed in various fields of medical image processing, astronomical image processing, satellite image processing, infrared image processing, etc. For example, histogram equalization is a useful method in X-ray image processing because it enhances the details of an X-ray image significantly to e.g. detect tumors easily.
Although several conventional methods for contrast enhancement exist, their primary application is limited to still images. Direct applications of such methods to moving images results in visual artifacts such as unnatural appearance or over-enhancement. Hence, such methods are not suitable for consumer products such as TV whose primary content is a sequence of images. Therefore, there is a need for a contrast enhancement method that is applicable to a video sequence which inherently has time-varying characteristics.